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Improved atmospheric correction and chlorophyll-a remote sensing models for turbid waters in a dusty environment / Maryam R. Al Shehhi in ISPRS Journal of photogrammetry and remote sensing, vol 133 (November 2017)
[article]
Titre : Improved atmospheric correction and chlorophyll-a remote sensing models for turbid waters in a dusty environment Type de document : Article/Communication Auteurs : Maryam R. Al Shehhi, Auteur ; Imen Gherboidj, Auteur ; Hosni Gherida, Auteur Année de publication : 2017 Article en page(s) : pp 46 - 60 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] Arabie
[Termes IGN] chlorophylle
[Termes IGN] correction atmosphérique
[Termes IGN] couleur de l'océan
[Termes IGN] eau de mer
[Termes IGN] image Aqua-MODIS
[Termes IGN] image Terra-MODIS
[Termes IGN] test de performance
[Termes IGN] turbidité océaniqueRésumé : (Auteur) This study presents a comprehensive assessment of the performance of the commonly used atmospheric correction models (NIR, SWIR, NIR-SWIR and FM) and ocean color products (OC3 and OC2) derived from MODIS images over the Arabian Gulf, Sea of Oman, and Arabian Sea. The considered atmospheric correction models have been used to derive MODIS normalized water-leaving radiances (nLw), which are compared to in situ water nLw(λ) data collected at different locations by Masdar Institute, United Arab of Emirates, and from AERONET-OC (the ocean color component of the Aerosol Robotic Network) database. From this comparison, the NIR model has been found to be the best performing model among the considered atmospheric correction models, which in turn shows disparity, especially at short wavelengths (400–500 nm) under high aerosol optical depth conditions (AOT (869) > 0.3) and over turbid waters. To reduce the error induced by these factors, a modified model taking into consideration the atmospheric and water turbidity conditions has been proposed. A turbidity index was used to identify the turbid water and a threshold of AOT (869) = 0.3 was used to identify the dusty atmosphere. Despite improved results in the MODIS nLw(λ) using the proposed approach, Chl-a models (OC3 and OC2) show low performance when compared to the in situ Chl-a measurements collected during several field campaigns organized by local, regional and international organizations. This discrepancy might be caused by the improper parametrization of these models or/and the improper selection of bands. Thus, an adaptive power fit algorithm (R2 = 0.95) has been proposed to improve the estimation of Chl-a concentration from 0.07 to 10 mg/m3 by using a new blue/red MODIS band ratio of (443,488)/645 instead of the default band ratio used for OC3(443,488)/547. The selection of this new band ratio (443,488)/645 has been based on using band 645 nm which has been found to represent both water turbidity and algal absorption. Numéro de notice : A2017-721 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.09.011 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.09.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88406
in ISPRS Journal of photogrammetry and remote sensing > vol 133 (November 2017) . - pp 46 - 60[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017111 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017112 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt 081-2017113 DEP-EXM Revue Saint-Mandé Dépôt en unité Exclu du prêt Monitoring surface urban heat island formation in a tropical mountain city using Landsat data (1987–2015) / Ronald C. Estoque in ISPRS Journal of photogrammetry and remote sensing, vol 133 (November 2017)
[article]
Titre : Monitoring surface urban heat island formation in a tropical mountain city using Landsat data (1987–2015) Type de document : Article/Communication Auteurs : Ronald C. Estoque, Auteur ; Yuji Murayama, Auteur Année de publication : 2017 Article en page(s) : pp 18 - 29 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] aide à la décision
[Termes IGN] analyse diachronique
[Termes IGN] Asie du sud-est
[Termes IGN] climat tropical
[Termes IGN] couvert végétal
[Termes IGN] ilot thermique urbain
[Termes IGN] image Landsat
[Termes IGN] montagne
[Termes IGN] Philippines
[Termes IGN] surface imperméable
[Termes IGN] surveillance de l'urbanisation
[Termes IGN] température de surface
[Termes IGN] urbanisme
[Termes IGN] villeRésumé : (Auteur) Since it was first described about two centuries ago and due to its adverse impacts on urban ecological environment and the overall livability of cities, the urban heat island (UHI) phenomenon has been, and still is, an important research topic across various fields of study. However, UHI studies on cities in mountain regions are still lacking. This study aims to contribute to this endeavor by monitoring and examining the formation of surface UHI (SUHI) in a tropical mountain city of Southeast Asia –Baguio City, the summer capital of the Philippines– using Landsat data (1987–2015). Based on mean surface temperature difference between impervious surface (IS) and green space (GS1), SUHI intensity (SUHII) in the study area increased from 2.7 °C in 1987 to 3.4 °C in 2015. Between an urban zone (>86% impervious) and a rural zone ( Numéro de notice : A2017-720 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.09.008 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.09.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88405
in ISPRS Journal of photogrammetry and remote sensing > vol 133 (November 2017) . - pp 18 - 29[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017111 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017112 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt 081-2017113 DEP-EXM Revue Saint-Mandé Dépôt en unité Exclu du prêt Nonlinear bias compensation of ZiYuan-3 satellite imagery with cubic splines / Jinshan Cao in ISPRS Journal of photogrammetry and remote sensing, vol 133 (November 2017)
[article]
Titre : Nonlinear bias compensation of ZiYuan-3 satellite imagery with cubic splines Type de document : Article/Communication Auteurs : Jinshan Cao, Auteur ; Jianhong Fu, Auteur ; Xiuxiao Yuan, Auteur ; Jianya Gong, Auteur Année de publication : 2017 Article en page(s) : pp 174 - 185 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] compensation non linéaire
[Termes IGN] correction géométrique
[Termes IGN] erreur systématique
[Termes IGN] image ZiYuan-3
[Termes IGN] modèle par fonctions rationnelles
[Termes IGN] orientation du capteur
[Termes IGN] point d'appui
[Termes IGN] résidu
[Termes IGN] spline cubique
[Termes IGN] transformation affineRésumé : (Auteur) Like many high-resolution satellites such as the ALOS, MOMS-2P, QuickBird, and ZiYuan1-02C satellites, the ZiYuan-3 satellite suffers from different levels of attitude oscillations. As a result of such oscillations, the rational polynomial coefficients (RPCs) obtained using a terrain-independent scenario often have nonlinear biases. In the sensor orientation of ZiYuan-3 imagery based on a rational function model (RFM), these nonlinear biases cannot be effectively compensated by an affine transformation. The sensor orientation accuracy is thereby worse than expected. In order to eliminate the influence of attitude oscillations on the RFM-based sensor orientation, a feasible nonlinear bias compensation approach for ZiYuan-3 imagery with cubic splines is proposed. In this approach, no actual ground control points (GCPs) are required to determine the cubic splines. First, the RPCs are calculated using a three-dimensional virtual control grid generated based on a physical sensor model. Second, one cubic spline is used to model the residual errors of the virtual control points in the row direction and another cubic spline is used to model the residual errors in the column direction. Then, the estimated cubic splines are used to compensate the nonlinear biases in the RPCs. Finally, the affine transformation parameters are used to compensate the residual biases in the RPCs. Three ZiYuan-3 images were tested. The experimental results showed that before the nonlinear bias compensation, the residual errors of the independent check points were nonlinearly biased. Even if the number of GCPs used to determine the affine transformation parameters was increased from 4 to 16, these nonlinear biases could not be effectively compensated. After the nonlinear bias compensation with the estimated cubic splines, the influence of the attitude oscillations could be eliminated. The RFM-based sensor orientation accuracies of the three ZiYuan-3 images reached 0.981 pixels, 0.890 pixels, and 1.093 pixels, which were respectively 42.1%, 48.3%, and 54.8% better than those achieved before the nonlinear bias compensation. Numéro de notice : A2017-725 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.10.007 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.10.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88410
in ISPRS Journal of photogrammetry and remote sensing > vol 133 (November 2017) . - pp 174 - 185[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017111 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017112 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt 081-2017113 DEP-EXM Revue Saint-Mandé Dépôt en unité Exclu du prêt Remote sensing of species diversity using Landsat 8 spectral variables / Sabelo Madonsela in ISPRS Journal of photogrammetry and remote sensing, vol 133 (November 2017)
[article]
Titre : Remote sensing of species diversity using Landsat 8 spectral variables Type de document : Article/Communication Auteurs : Sabelo Madonsela, Auteur ; Moses Azong Cho, Auteur ; Abel Ramoleo, Auteur ; Onisimo Mutanga, Auteur Année de publication : 2017 Article en page(s) : pp 116 - 127 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Afrique du sud (état)
[Termes IGN] analyse en composantes principales
[Termes IGN] bande infrarouge
[Termes IGN] biodiversité
[Termes IGN] espèce végétale
[Termes IGN] image Landsat-8
[Termes IGN] image Landsat-OLI
[Termes IGN] indice de diversité
[Termes IGN] indice de végétation
[Termes IGN] matrice de co-occurrence
[Termes IGN] régression linéaire
[Termes IGN] savaneRésumé : (Auteur) The application of remote sensing in biodiversity estimation has largely relied on the Normalized Difference Vegetation Index (NDVI). The NDVI exploits spectral information from red and near infrared bands of Landsat images and it does not consider canopy background conditions hence it is affected by soil brightness which lowers its sensitivity to vegetation. As such NDVI may be insufficient in explaining tree species diversity. Meanwhile, the Landsat program also collects essential spectral information in the shortwave infrared (SWIR) region which is related to plant properties. The study was intended to: (i) explore the utility of spectral information across Landsat-8 spectrum using the Principal Component Analysis (PCA) and estimate alpha diversity (α-diversity) in the savannah woodland in southern Africa, and (ii) define the species diversity index (Shannon (H′), Simpson (D2) and species richness (S) – defined as number of species in a community) that best relates to spectral variability on the Landsat-8 Operational Land Imager dataset. We designed 90 m × 90 m field plots (n = 71) and identified all trees with a diameter at breast height (DbH) above 10 cm. H′, D2 and S were used to quantify tree species diversity within each plot and the corresponding spectral information on all Landsat-8 bands were extracted from each field plot. A stepwise linear regression was applied to determine the relationship between species diversity indices (H′, D2 and S) and Principal Components (PCs), vegetation indices and Gray Level Co-occurrence Matrix (GLCM) texture layers with calibration (n = 46) and test (n = 23) datasets. The results of regression analysis showed that the Simple Ratio Index derivative had a higher relationship with H′, D2 and S (r2 = 0.36; r2 = 0.41; r2 = 0.24 respectively) compared to NDVI, EVI, SAVI or their derivatives. Moreover the Landsat-8 derived PCs also had a higher relationship with H′ and D2 (r2 of 0.36 and 0.35 respectively) than the frequently used NDVI, and this was attributed to the utilization of the entire spectral content of Landsat-8 data. Our results indicate that: (i) the measurement scales of vegetation indices impact their sensitivity to vegetation characteristics and their ability to explain tree species diversity; (ii) principal components enhance the utility of Landsat-8 spectral data for estimating tree species diversity and (iii) species diversity indices that consider both species richness and abundance (H′ and D2) relates better with Landsat-8 spectral variables. Numéro de notice : A2017-723 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.10.008 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.10.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88408
in ISPRS Journal of photogrammetry and remote sensing > vol 133 (November 2017) . - pp 116 - 127[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017111 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017112 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt 081-2017113 DEP-EXM Revue Saint-Mandé Dépôt en unité Exclu du prêt Hyperspectral dimensionality reduction for biophysical variable statistical retrieval / Juan Pablo Rivera-Caicedo in ISPRS Journal of photogrammetry and remote sensing, vol 132 (October 2017)
[article]
Titre : Hyperspectral dimensionality reduction for biophysical variable statistical retrieval Type de document : Article/Communication Auteurs : Juan Pablo Rivera-Caicedo, Auteur ; Jochem Verrelst, Auteur ; Jordi Munoz-Mari, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 88 - 101 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] image HYMAP
[Termes IGN] image hyperspectrale
[Termes IGN] Leaf Area Index
[Termes IGN] régression linéaire
[Termes IGN] variable biophysique (végétation)Résumé : (Auteur) Current and upcoming airborne and spaceborne imaging spectrometers lead to vast hyperspectral data streams. This scenario calls for automated and optimized spectral dimensionality reduction techniques to enable fast and efficient hyperspectral data processing, such as inferring vegetation properties. In preparation of next generation biophysical variable retrieval methods applicable to hyperspectral data, we present the evaluation of 11 dimensionality reduction (DR) methods in combination with advanced machine learning regression algorithms (MLRAs) for statistical variable retrieval. Two unique hyperspectral datasets were analyzed on the predictive power of DR + MLRA methods to retrieve leaf area index (LAI): (1) a simulated PROSAIL reflectance data (2101 bands), and (2) a field dataset from airborne HyMap data (125 bands). For the majority of MLRAs, applying first a DR method leads to superior retrieval accuracies and substantial gains in processing speed as opposed to using all bands into the regression algorithm. This was especially noticeable for the PROSAIL dataset: in the most extreme case, using the classical linear regression (LR), validation results (RMSECV) improved from 0.06 (12.23) without a DR method to 0.93 (0.53) when combining it with a best performing DR method (i.e., CCA or OPLS). However, these DR methods no longer excelled when applied to noisy or real sensor data such as HyMap. Then the combination of kernel CCA (KCCA) with LR, or a classical PCA and PLS with a MLRA showed more robust performances ( of 0.93). Gaussian processes regression (GPR) uncertainty estimates revealed that LAI maps as trained in combination with a DR method can lead to lower uncertainties, as opposed to using all HyMap bands. The obtained results demonstrated that, in general, biophysical variable retrieval from hyperspectral data can largely benefit from dimensionality reduction in both accuracy and computational efficiency. Numéro de notice : A2017-640 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.08.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.08.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86995
in ISPRS Journal of photogrammetry and remote sensing > vol 132 (October 2017) . - pp 88 - 101[article]Réservation
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